Early flood warnings from empirical (expanded) downscaling of the full ECMWF Ensemble Prediction System
Bibliographic record
Abstract
A prototype early warning system for floods is introduced. For a small headwater catchment, probabilistic streamflow predictions in 24‐hourly steps are obtained from downscaling all members of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Ensemble Prediction System and feeding the resulting precipitation and temperature series into a hydrologic model. We apply “expanded downscaling,” a scheme that was previously used for climate scenarios and that is particularly suited to extreme events and the simulation of flood‐triggering heavy rainfall. The entire model chain is thoroughly verified, using daily precipitation and streamflow observations and forecasts from the decade 1997–2006. It turns out that strong meteorologic (precipitation) events are skillfully predicted for at least 5 days lead time by the downscaling. That skill, however, is partly lost by deficiencies in the hydrological modeling as revealed in this study. We discuss ways to overcome these difficulties, along with the prospect of employing the whole system operationally, for example, for reservoir regulations. We close with an outlook for early flash flood warnings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".